Chase

At Bell Labs in the late 1940s, where most doors were closed and equations were treated like weapons-grade material, one researcher rode a unicycle down the hallway and juggled to clear his mind. That was Claude Shannon. From the outside, it did not look like serious work. Bell Labs was a place built on rigor, deadlines, and problems tied directly to the future of communication systems. The stakes were not abstract. Telephone networks had to scale. Signals had to survive noise. Every improvement mattered. Across the hall, Richard Hamming watched all this with a mix of curiosity and skepticism. Hamming was disciplined, methodical, and deeply focused on getting results. He believed in hard problems and sustained attention. And yet, here was Shannon, building gadgets, playing with mechanical toys, riding that unicycle, and seemingly drifting between ideas. At first glance, it raised an uncomfortable question. How much real work could someone like that actually be doing? But Bell Labs had a way of revealing answers slowly. Conversations mattered there. Hallways mattered. Ideas did not stay confined to notebooks. Hamming began to notice something. Shannon’s door was almost always open. People walked in and out constantly. Engineers, physicists, mathematicians. Problems were discussed casually, often without immediate purpose. Bits of circuit design, logic, probability, switching systems, all mixed together in loose, unfinished conversations. It looked unfocused. It was anything but. Hamming would later describe a pattern he saw clearly over time. Many researchers kept their doors closed. They believed it helped them concentrate, to push forward on well-defined problems without interruption. And for a while, it worked. They produced solid, incremental results. But the world moved on. The problems evolved. The frameworks changed. And those who stayed locked inside their narrow focus often found themselves working on questions that were no longer central. Shannon operated differently. His open door was not a lack of discipline. It was a deliberate exposure to ideas. He allowed interruptions because they carried information. Each conversation was a small input from another domain. Over time, those inputs began to connect. What looked like distraction was actually cross-pollination. By 1948, those scattered threads came together in a single, decisive leap. Shannon published A Mathematical Theory of Communication. In it, he connected ideas that had existed separately: the logic of switching circuits, rooted in George Boole’s algebra; the physical reality of communication systems; and the probabilistic concept of Entropy. He reframed communication itself. Not as signals in wires, but as information that could be measured, encoded, transmitted, and recovered despite noise. The work did not just solve existing problems. It defined an entirely new field, Information Theory. From the outside, it might have looked like Shannon had been wandering. In reality, he had been working on the right problem. Hamming took that lesson seriously. He began to distinguish between doing work and doing important work. Many people, he realized, stay busy solving well-posed problems that lead to incremental progress. It is safe, productive, and often rewarded. But Great Work requires something else. It requires choosing problems that matter, even if they are not clearly defined yet. It requires tolerating periods that look unproductive. It requires, at times, an open door. Hamming would later reflect that those who shut themselves away to “get work done” often missed the larger shift happening around them. Meanwhile, someone like Shannon, who seemed to be frittering away time, was quietly assembling the pieces of a new intellectual framework. Bell Labs did not just produce answers. It revealed a deeper rule. It is not enough to work hard. You have to work on the right things, even when it does not look like work at all.

PhilosophyOfPhy's tweet photo. At Bell Labs in the late 1940s, where most doors were closed and equations were treated like weapons-grade material, one researcher rode a unicycle down the hallway and juggled to clear his mind.

That was Claude Shannon.

From the outside, it did not look like serious work. Bell Labs was a place built on rigor, deadlines, and problems tied directly to the future of communication systems.

The stakes were not abstract. Telephone networks had to scale. Signals had to survive noise. Every improvement mattered.

Across the hall, Richard Hamming watched all this with a mix of curiosity and skepticism. Hamming was disciplined, methodical, and deeply focused on getting results. He believed in hard problems and sustained attention.

And yet, here was Shannon, building gadgets, playing with mechanical toys, riding that unicycle, and seemingly drifting between ideas.

At first glance, it raised an uncomfortable question. How much real work could someone like that actually be doing?

But Bell Labs had a way of revealing answers slowly. Conversations mattered there. Hallways mattered. Ideas did not stay confined to notebooks.

Hamming began to notice something. Shannon’s door was almost always open.

People walked in and out constantly. Engineers, physicists, mathematicians. Problems were discussed casually, often without immediate purpose.

Bits of circuit design, logic, probability, switching systems, all mixed together in loose, unfinished conversations. It looked unfocused. It was anything but.

Hamming would later describe a pattern he saw clearly over time. Many researchers kept their doors closed.

They believed it helped them concentrate, to push forward on well-defined problems without interruption. And for a while, it worked. They produced solid, incremental results.

But the world moved on.

The problems evolved. The frameworks changed. And those who stayed locked inside their narrow focus often found themselves working on questions that were no longer central.

Shannon operated differently. His open door was not a lack of discipline. It was a deliberate exposure to ideas.

He allowed interruptions because they carried information. Each conversation was a small input from another domain. Over time, those inputs began to connect.

What looked like distraction was actually cross-pollination.

By 1948, those scattered threads came together in a single, decisive leap. Shannon published A Mathematical Theory of Communication.

In it, he connected ideas that had existed separately: the logic of switching circuits, rooted in George Boole’s algebra; the physical reality of communication systems; and the probabilistic concept of Entropy.

He reframed communication itself. Not as signals in wires, but as information that could be measured, encoded, transmitted, and recovered despite noise.

The work did not just solve existing problems. It defined an entirely new field, Information Theory.

From the outside, it might have looked like Shannon had been wandering. In reality, he had been working on the right problem.

Hamming took that lesson seriously. He began to distinguish between doing work and doing important work.

Many people, he realized, stay busy solving well-posed problems that lead to incremental progress. It is safe, productive, and often rewarded.

But Great Work requires something else. It requires choosing problems that matter, even if they are not clearly defined yet.

It requires tolerating periods that look unproductive. It requires, at times, an open door.

Hamming would later reflect that those who shut themselves away to “get work done” often missed the larger shift happening around them.

Meanwhile, someone like Shannon, who seemed to be frittering away time, was quietly assembling the pieces of a new intellectual framework.

Bell Labs did not just produce answers. It revealed a deeper rule.

It is not enough to work hard.

You have to work on the right things, even when it does not look like work at all.

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ChaseMcMichael retweeted

Math Lady Hazel 🇦🇷

@mathladyhazel

5 months ago

What Quantum Physics does to a man.

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Chase @ChaseMcMichael

5 months ago

@BrianRoemmele This is a fact

Chase @ChaseMcMichael

6 months ago

@surajit_ghosh2 Its Odd they cant “fly” over the Apolo landing sites ahhh maybe tranquility bay? Would be nice to see that luner car and a few of those American flags or say the Bottom of the LEM.

Chase @ChaseMcMichael

6 months ago

@matthew_pines yes this is real but its not terrify dog whistleblower BS it absolutely about time we go at speed with science and discovery

ChaseMcMichael retweeted

Math Files

@Math_files

6 months ago

257 years ago, Joseph Fourier was born on this day. In 1822, Fourier showed that any wave can be broken down into an infinite sum of sine waves, using a technique now called the Fourier transform. The Fourier transform is like a recipe generator. You input a complicated wave and you get back its ingredients, the amplitude and frequency of each component sine wave. Fourier analysis is an essential part of modern technology. Its applications range from JPEG compression and image recognition to quantum physics and MRI's. Fourier theory has two components: basic building blocks and labels. Imagine a child's toy castle. This castle can be disassembled into individual building blocks. And these building blocks can then be sorted by color into bins and labeled. Similarly, the Fourier transform disassembles a complex wave into individual sine waves. These are like the building blocks. Each sine wave can be labeled with its frequency, or how quickly it oscillates per second. The labels on the bins are more than just a way to organize things. They can be used to rebuild the original complex wave and as an efficient shorthand for communicating information. For example, when you send a voice message, your phone doesn't transmit an entire complex sound wave. Instead, it breaks it down and sends just the labels or frequencies of the component sine waves. The receiver's phone then reverses this process, converting the labels back into the contents of the bins to reconstruct the message’s original sound wave.

Math_files's tweet photo. 257 years ago, Joseph Fourier was born on this day.

In 1822, Fourier showed that any wave can be broken down into an infinite sum of sine waves, using a technique now called the Fourier transform.

The Fourier transform is like a recipe generator. You input a complicated wave and you get back its ingredients, the amplitude and frequency of each component sine wave.

Fourier analysis is an essential part of modern technology. Its applications range from JPEG compression and image recognition to quantum physics and MRI's.

Fourier theory has two components: basic building blocks and labels. Imagine a child's toy castle. This castle can be disassembled into individual building blocks. And these building blocks can then be sorted by color into bins and labeled.

Similarly, the Fourier transform disassembles a complex wave into individual sine waves. These are like the building blocks.

Each sine wave can be labeled with its frequency, or how quickly it oscillates per second. The labels on the bins are more than just a way to organize things.

They can be used to rebuild the original complex wave and as an efficient shorthand for communicating information.

For example, when you send a voice message, your phone doesn't transmit an entire complex sound wave. Instead, it breaks it down and sends just the labels or frequencies of the component sine waves.

The receiver's phone then reverses this process, converting the labels back into the contents of the bins to reconstruct the message’s original sound wave.

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Chase @ChaseMcMichael

7 months ago

@ZubyMusic @arrington @ClareCraigPath and quality 100x stronger in the past

ChaseMcMichael retweeted

Elon Musk

@elonmusk

7 months ago

😑

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Chase @ChaseMcMichael

7 months ago

@pushmeet @GoogleDeepMind Thank you

Chase @ChaseMcMichael

7 months ago

@AwakenWithJP @ConceptualJames comment on this post and see if your engagement is zero this could be a good way to test

Chase @ChaseMcMichael

7 months ago

@ConceptualJames James are you being shadowed like your engagement has gone to zero

Chase @ChaseMcMichael

7 months ago

@Leo_Gross_IBM This is very nice

ChaseMcMichael retweeted

Dustin

@r0ck3t23

7 months ago

Adding more GPUs will never make a machine conscious. Nobel Prize-winning physicist Roger Penrose just dismantled the entire AI race’s core assumption. Right now, the industry operates on one belief. Build massive data centers. Scale the models. AGI will just “wake up.” Penrose destroys this completely. Penrose: “There is this sort of view that once you make a computer complicated enough or something, it suddenly becomes aware. I just don’t believe that. There’s no reason to believe that.” A machine can compute better than any human alive. But computation is not awareness. Penrose: “There is something quite different involved in understanding things, in being aware of things, of feeling things, which is not part of computations.” We’re confusing rule-following with actual intelligence. Penrose: “The keyword is the word ‘understanding.’ You can follow rules alright, but we don’t understand what we’re doing. The understanding is the key point.” Models today are exceptional at processing data. At mimicking logic. But true understanding requires consciousness. Penrose: “It doesn’t make sense to say of a device that it understands something if it’s not even aware of it. There is something much more profound in being conscious of something.” And here’s what should terrify every AI lab on earth. Penrose: “I believe that the brain is following the laws of physics, sure. We don’t have a good picture of the laws of physics.” Penrose: “Quantum mechanics is not an answer to the way the universe operates. It’s a partial answer. It’s incomplete.” We’re trying to engineer synthetic consciousness using classical computation. While biological consciousness likely operates on physics we haven’t even discovered yet. The race to AGI isn’t just an engineering problem. It’s a frontier science problem. The labs are hiring engineers. The problem might require physicists who don’t exist yet.

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ChaseMcMichael retweeted

Liz Parrish MBA

@ParrishLiz

7 months ago

Known as cyclin D-binding myb-like transcription factor 1 or DMTF1, the scientists found that this protein's levels are repressed in the “aged” neural stem cells and that restoring it is sufficient to restore the regeneration capabilities of such neural stem cells. https://t.co/cdM5oICbNH